Sustainable Healthcare System for Pharmaceutical Waste Reduction and Smart Medical Waste Identification
Abstract
We suggest that health care systems based on AI are more sustainable because they can help with classification and management of waste produced from medical and pharmaceutical sources. In this regard, we make use of machine learning algorithms to automate the categorization of medical waste based upon whether it falls into one of four possible categories: (1) Biodegradable, (2) recyclable, (3) hazardous & (4) pharmaceutical. Computer vision (image processing) techniques and deep learning, in particular CNNs using MobileNetV2 models have been developed to classify various types of medical waste to achieve accurate results. In addition, we have developed a module to analyze past data and provide forecasts regarding future demand for pharmaceutical waste; the module will assist hospitals with decision making related to how much space they should reserve for storing usable pharmaceuticals. Users will also be able to make informed decisions through the user-friendly interface we will provide by employing these techniques within a circular model based upon the principles of sustainability.
Citations
IRE Journals:
Dr. G. Sangeetha, V. Abhishek, A.E.T. Baskaran, V. Bhargav "Sustainable Healthcare System for Pharmaceutical Waste Reduction and Smart Medical Waste Identification" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 243-248 https://doi.org/10.64388/IREV9I10-1715930
IEEE:
Dr. G. Sangeetha, V. Abhishek, A.E.T. Baskaran, V. Bhargav
"Sustainable Healthcare System for Pharmaceutical Waste Reduction and Smart Medical Waste Identification" Iconic Research And Engineering Journals, 9(10) https://doi.org/10.64388/IREV9I10-1715930
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
Important Links
For Authors
Volume 8:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 7:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 6:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 5:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 4:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 3:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12